ML Infrastructure and Performance Engineer, AI Expert

Job not on LinkedIn

🔥 13 minutes ago

🇺🇸 United States – Remote

💵 $90 - $120 / hour

⏰ Full Time

🟢 Junior

🟡 Mid-level

🤖 AI Engineer

🚫👨‍🎓 No degree required

👻 Ghost score 20%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.Why this score:

• Just posted.
• Jobs at this company usually fill in 33 days.
• 130 open roles for a team of 134.

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Weekday (YC W21)

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

💼 Consulting

👥 HR Tech

☁️ SaaS

Consulting • HR Tech • SaaS

Weekday is a modern recruitment platform that combines AI technologies with a vast database of potential candidates, aiming to streamline the hiring process for companies in India. They offer various services, including a proactive outreach approach that helps employers connect with top talent, as well as tools for candidates to easily apply for jobs. Weekday's emphasis on candidate engagement through multiple channels, including email, WhatsApp, and phone calls, sets it apart in the competitive landscape of recruitment agencies.

📋 Description

• Design challenging technical tasks covering accelerator kernel development, performance profiling, distributed workload debugging, and high-throughput inference serving • Develop complex and adversarial engineering problems testing advanced AI model capabilities in accelerator programming and optimization • Create accurate, technically sound reference solutions demonstrating best practices in ML systems engineering • Evaluate AI-generated solutions for correctness, efficiency, scalability, and practical applicability • Develop technical guidelines and assessment rubrics for kernel optimization, profiler interpretation, distributed systems, and inference performance • Analyze performance bottlenecks across ML infrastructure, compilers, accelerators, and model-serving systems • Provide structured technical feedback to improve AI model capabilities in ML systems and infrastructure engineering • Collaborate with researchers and subject matter experts to maintain the quality, consistency, and accuracy of AI training and evaluation data

🎯 Requirements

• At least 2 years of hands-on professional experience in ML systems, ML infrastructure, model serving, accelerator performance engineering, or a closely related field • Practical experience in accelerator kernel programming, performance profiling, distributed workload debugging, or LLM inference serving • Experience with Pallas, Triton, CUDA, CUTLASS, or similar technologies, as applicable • Experience with JAX/XLA Profiler, Kineto, torch.profiler, or NVIDIA Nsight, as applicable • Experience with vLLM, SGLang, TensorRT-LLM, JetStream, KV cache optimization, paged attention, or continuous batching, as applicable • Production-level experience with JAX and/or PyTorch • Understanding of modern accelerator architectures, including TPUs v4–v6e, NVIDIA H100/B200, AWS Trainium, or AMD MI300 • Ability to analyze and optimize throughput, latency, memory utilization, and computational efficiency • Demonstrable professional growth and career progression in engineering or infrastructure-focused roles • Availability to commit at least 40 hours per week on weekdays • Excellent written communication skills and ability to explain complex technical concepts clearly • Preferred: experience with JAX, XLA, and TPU-based development environments • Preferred: knowledge of custom operators, sharding, and distributed training frameworks such as pjit, shard_map, FSDP, Megatron, or MaxText • Preferred: familiarity with XLA, StableHLO, torch.compile, and MLIR • Preferred: experience developing performance benchmarks, technical evaluation frameworks, or engineering assessment rubrics • Preferred: exposure to AI model evaluation, synthetic training data generation, or frontier AI systems • Candidates must commit exclusively to this engagement without conflicting professional commitments

🏖️ Benefits

• Fully remote work arrangement • Full-time engagement of 40 hours per week • Weekday schedule • Employment or engagement through an appropriate local partner, depending on the candidate's country of residence

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